Improve dataset card for M2RAG benchmark

This PR updates the dataset card for M2RAG to enhance its discoverability and utility.

Key improvements include:
- Correcting `task_categories` to `image-text-to-text` to better reflect the dataset's multi-modal text generation tasks, while retaining `visual-question-answering`.
- Adding `retrieval-augmented-generation`, `multimodal`, and `benchmark` as relevant tags for improved searchability.
- Enhancing the overview with details about `MM-RAIT` from the paper abstract and GitHub README.
- Incorporating a comprehensive "Sample Usage" section, porting detailed instructions for setup, reproduction, and evaluation from the GitHub repository to guide users effectively.

README.md

@@ -1,19 +1,24 @@
 ---
+language:
+- en
 license: mit
 task_categories:
-- text-to-image
 - visual-question-answering
-language:
-- en
+- image-text-to-text
+tags:
+- retrieval-augmented-generation
+- multimodal
+- benchmark
 ---
-# Data statices of M2RAG
+
+# M2RAG: Benchmarking Retrieval-Augmented Generation in Multi-Modal Contexts
 
 Click the links below to view our paper and Github project.
 <a href='https://arxiv.org/abs/2502.17297'><img src='https://img.shields.io/badge/Paper-Arxiv-red'></a><a href='https://github.com/NEUIR/M2RAG'><img src="https://img.shields.io/badge/Github-M2RAG-blue?logo=Github"></a>
 
-If you find this work useful, please cite our paper  and give us a shining star 🌟 in Github 
+If you find this work useful, please cite our paper and give us a shining star 🌟 in Github
 
-```
+```bibtex
 @misc{liu2025benchmarkingretrievalaugmentedgenerationmultimodal,
       title={Benchmarking Retrieval-Augmented Generation in Multi-Modal Contexts}, 
       author={Zhenghao Liu and Xingsheng Zhu and Tianshuo Zhou and Xinyi Zhang and Xiaoyuan Yi and Yukun Yan and Yu Gu and Ge Yu and Maosong Sun},
@@ -24,10 +29,13 @@ If you find this work useful, please cite our paper  and give us a shining star
       url={https://arxiv.org/abs/2502.17297}, 
 }
 ```
+
 ## 🎃 Overview
 
 The **M²RAG** benchmark evaluates Multi-modal Large Language Models (MLLMs) by using multi-modal retrieved documents to answer questions. It includes four tasks: image captioning, multi-modal QA, fact verification, and image reranking, assessing MLLMs’ ability to leverage knowledge from multi-modal contexts. 
 
+The **Multi-Modal Retrieval Augmented Instruction Tuning (MM-RAIT)** method further adapts MLLMs to multi-modal in-context learning, enhancing their effectiveness in utilizing knowledge from these retrieval documents.
+
 <p align="center">
   <img align="middle" src="https://raw.githubusercontent.com/NEUIR/M2RAG/main/assets/m2rag.png" style="width: 600px;" alt="m2rag"/>
 </p>
@@ -46,6 +54,123 @@ M2RAG/
 
 ❗️Note: 
 
-- If you encounter difficulties when downloading the images directly, please download and use the pre-packaged image file ```M2RAG_Images.zip``` instead.
+- If you encounter difficulties when downloading the images directly, please download and use the pre-packaged image file `M2RAG_Images.zip` instead.
+
+- To obtain the `imgs.tsv`, you can follow the instructions in the [WebQA](https://github.com/WebQnA/WebQA?tab=readme-ov-file#download-data) project. Specifically, you need to first download all the data from the folder [WebQA_imgs_7z_chunks](https://drive.google.com/drive/folders/19ApkbD5w0I5sV1IeQ9EofJRyAjKnA7tb), and then run the command `7z x imgs.7z.001` to unzip and merge all chunks to get the imgs.tsv.
+
+## 🎃 Sample Usage
+
+### 🌵 Requirements
+To use this dataset and reproduce results, install the following packages using Pip or Conda:
+```
+Python==3.10
+Pytorch
+transformers==4.44.2 (4.46.1 for finetune qwen2-vl)
+clip
+faiss==1.9.0
+tqdm
+numpy
+base64
+diffusers
+flash-attn
+xformers
+llamafactory
+accelerate
+nltk
+rouge_score
+sklearn
+```
+We provide the version file `requirements.txt` of all our used packages in the GitHub repository for environment configuration.
+
+You will also need pretrained models: [MiniCPM-V 2.6](https://huggingface.co/openbmb/MiniCPM-V-2_6), [Qwen2-VL](https://huggingface.co/Qwen/Qwen2-VL-7B-Instruct), and [VISTA](https://huggingface.co/BAAI/bge-visualized) (used for multi-modal document retrieval).
+
+### 🌵 Reproduce MM-RAIT
+
+#### Download Code & Dataset
+First, clone the project from GitHub:
+```bash
+git clone https://github.com/NEUIR/M2RAG
+cd M2RAG
+```
+Second, you can either directly download and use [M2RAG](https://huggingface.co/datasets/whalezzz/M2RAG), or follow the instructions in 'data/data_preprocess' to build it step by step. Please place the downloaded dataset in the `data` folder as shown in the data structure above.
+(❗️Note: For the ```imgs.tsv```, you need to download the data from [this link](https://drive.google.com/drive/folders/1ApfD-RzvJ79b-sLeBx1OaiPNUYauZdAZ?usp=sharing) and run ```7z x imgs.7z.001```).
+
+```
+data/
+└──m2rag/
+    ├──fact_verify/
+    ├──image_cap/
+    ├──image_rerank/
+    ├──mmqa/
+    ├──imgs.lineidx.new
+    └──imgs.tsv
+```
+
+#### Inference for Zero-Shot setting
+Once the dataset and vanilla models are ready, you can follow the instructions below to reproduce our zero-shot results.
+
+* Step 1: Encode the queries from the test set and the multi-modal corpus for each task.
+```bash
+cd script
+bash get_embed_test.sh
+```
+
+* Step 2: Retrieve the topN most relevant multi-modal documents for each query.
+```bash
+bash retrieval_test.sh
+```
+* Step 3: Use the retrieved documents for vanilla RAG inference.
+```bash
+bash inference_cpmv.sh # or bash inference_qwen.sh
+```
+For Image Reranking task, please use:
+```bash
+bash compute_ppl_minicpmv.sh # or bash compute_ppl_qwen2vl.sh
+```
+
+#### Train MM-RAIT
+**Using the MiniCPM-V 2.6 models as an example, I will show you how to reproduce the results in this paper. The same is true for the Qwen2-VL. Also, we provide fine-tuned checkpoints. You can skip this step and proceed directly to inference.**
 
-- To obtain the ```imgs.tsv```, you can follow the instructions in the [WebQA](https://github.com/WebQnA/WebQA?tab=readme-ov-file#download-data) project. Specifically, you need to first download all the data from the folder [WebQA_imgs_7z_chunks](https://drive.google.com/drive/folders/19ApkbD5w0I5sV1IeQ9EofJRyAjKnA7tb), and then run the command ``` 7z x imgs.7z.001```to unzip and merge all chunks to get the imgs.tsv.
+* First step: Prepare the training data.
+```bash
+bash get_embed_train.sh
+bash retrieval_train.sh
+cd ../data/
+bash finetune/construct_finetune_data.sh
+```
+
+* Second step: Fine-tune the MiniCPM-V model using LoRA.
+```bash
+cd ../script
+bash finetune_cpmv.sh
+```
+
+* Final step: Use the fine-tuned model for inference.
+```bash
+bash inference_cpmv.sh
+```
+For Image Reranking task, please use:
+```bash
+bash compute_ppl_minicpmv.sh
+```
+
+### 🌵 Evaluate Generation Effectiveness
+Go to the `src/evaluation` folder and evaluate model performance as follows:
+* For Image Captioning and Multi-modal QA tasks, please use:
+```bash
+python generation.py --reference_file path_to_reference_data --candidate_file path_to_generation_data
+```
+* For Multi-Modal Fact Verification task, please use:
+```bash
+python classification.py --true_file path_to_reference_data --pred_file path_to_generation_data
+```
+* For Image Reranking task, please use:
+```bash
+python -m pytorch_fid path/to/reference_images path/to/rerank_images
+```
+
+## 🎃 Contact
+If you have questions, suggestions, and bug reports, please email:
+```
+[email protected]     [email protected] 
+```